





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
__UpperCAmelCase							=   logging.get_logger(__name__)

__UpperCAmelCase							=   {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''adapter_layer''': '''encoder.layers.*.adapter_layer''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
    '''pooling_layer.linear''': '''projector''',
    '''pooling_layer.projection''': '''classifier''',
}
__UpperCAmelCase							=   [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]
def       __UpperCamelCase					(      lowercase__						:							str		)						->   List[str]:
    '''simple docstring'''





    lowerCAmelCase_       :						List[str]							= {}
    with open(UpperCamelCase_		,     """r"""		) as file:
        for line_number, line in enumerate(UpperCamelCase_		):
            lowerCAmelCase_       :						Any							= line.strip()
            if line:
                lowerCAmelCase_       :						Optional[int]							= line.split()
                lowerCAmelCase_       :						List[str]							= line_number
                lowerCAmelCase_       :						Dict							= words[0]
                lowerCAmelCase_       :						Optional[int]							= value
    return result
def       __UpperCamelCase					(      lowercase__						:							Optional[int]		,     lowercase__						:							List[str]		,     lowercase__						:							List[Any]		,     lowercase__						:							Optional[Any]		,     lowercase__						:							Tuple		)						->   Any:
    '''simple docstring'''





    for attribute in key.split("""."""		):
        lowerCAmelCase_       :						List[str]							= getattr(UpperCamelCase_		,     UpperCamelCase_		)

    lowerCAmelCase_       :						Tuple							= None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(UpperCamelCase_		):
            lowerCAmelCase_       :						Any							= PARAM_MAPPING[full_name.split("""."""		)[-1]]
            lowerCAmelCase_       :						Tuple							= """param"""

    if weight_type is not None and weight_type != "param":
        lowerCAmelCase_       :						str							= getattr(UpperCamelCase_		,     UpperCamelCase_		).shape
    elif weight_type is not None and weight_type == "param":
        lowerCAmelCase_       :						Any							= hf_pointer
        for attribute in hf_param_name.split("""."""		):
            lowerCAmelCase_       :						str							= getattr(UpperCamelCase_		,     UpperCamelCase_		)
        lowerCAmelCase_       :						List[str]							= shape_pointer.shape

        # let's reduce dimension
        lowerCAmelCase_       :						Optional[int]							= value[0]
    else:
        lowerCAmelCase_       :						Optional[int]							= hf_pointer.shape

    if hf_shape != value.shape:
        raise ValueError(
            f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
            f' {value.shape} for {full_name}'		)

    if weight_type == "weight":
        lowerCAmelCase_       :						List[str]							= value
    elif weight_type == "weight_g":
        lowerCAmelCase_       :						Union[str, Any]							= value
    elif weight_type == "weight_v":
        lowerCAmelCase_       :						int							= value
    elif weight_type == "bias":
        lowerCAmelCase_       :						Dict							= value
    elif weight_type == "param":
        for attribute in hf_param_name.split("""."""		):
            lowerCAmelCase_       :						Optional[int]							= getattr(UpperCamelCase_		,     UpperCamelCase_		)
        lowerCAmelCase_       :						Optional[int]							= value
    else:
        lowerCAmelCase_       :						Tuple							= value

    logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'		)
def       __UpperCamelCase					(      lowercase__						:							str		,     lowercase__						:							Any		,     lowercase__						:							str		,     lowercase__						:							List[str]		,     lowercase__						:							List[Any]		)						->   Union[str, Any]:
    '''simple docstring'''





    lowerCAmelCase_       :						int							= None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(UpperCamelCase_		):
            lowerCAmelCase_       :						str							= PARAM_MAPPING[full_name.split("""."""		)[-1]]
            lowerCAmelCase_       :						List[Any]							= """param"""

    if weight_type is not None and weight_type != "param":
        lowerCAmelCase_       :						List[str]							= """.""".join([key, weight_type]		)
    elif weight_type is not None and weight_type == "param":
        lowerCAmelCase_       :						Optional[Any]							= """.""".join([key, hf_param_name]		)
    else:
        lowerCAmelCase_       :						Union[str, Any]							= key

    lowerCAmelCase_       :						Tuple							= value if """lm_head""" in full_key else value[0]


__UpperCAmelCase							=   {
    '''W_a''': '''linear_1.weight''',
    '''W_b''': '''linear_2.weight''',
    '''b_a''': '''linear_1.bias''',
    '''b_b''': '''linear_2.bias''',
    '''ln_W''': '''norm.weight''',
    '''ln_b''': '''norm.bias''',
}
def       __UpperCamelCase					(      lowercase__						:							str		,     lowercase__						:							str		,     lowercase__						:							Optional[Any]=None		,     lowercase__						:							Tuple=None		)						->   Optional[Any]:
    '''simple docstring'''





    lowerCAmelCase_       :						int							= False
    for key, mapped_key in MAPPING.items():
        lowerCAmelCase_       :						Dict							= """wav2vec2.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
        if key in name or key.split("""w2v_model."""		)[-1] == name.split("""."""		)[0]:
            lowerCAmelCase_       :						str							= True
            if "*" in mapped_key:
                lowerCAmelCase_       :						Any							= name.split(UpperCamelCase_		)[0].split("""."""		)[-2]
                lowerCAmelCase_       :						List[Any]							= mapped_key.replace("""*"""		,     UpperCamelCase_		)
            if "weight_g" in name:
                lowerCAmelCase_       :						List[str]							= """weight_g"""
            elif "weight_v" in name:
                lowerCAmelCase_       :						int							= """weight_v"""
            elif "bias" in name:
                lowerCAmelCase_       :						Any							= """bias"""
            elif "weight" in name:
                # TODO: don't match quantizer.weight_proj
                lowerCAmelCase_       :						Dict							= """weight"""
            else:
                lowerCAmelCase_       :						Any							= None
            if hf_dict is not None:
                rename_dict(UpperCamelCase_		,     UpperCamelCase_		,     UpperCamelCase_		,     UpperCamelCase_		,     UpperCamelCase_		)
            else:
                set_recursively(UpperCamelCase_		,     UpperCamelCase_		,     UpperCamelCase_		,     UpperCamelCase_		,     UpperCamelCase_		)
            return is_used
    return is_used
def       __UpperCamelCase					(      lowercase__						:							Optional[int]		,     lowercase__						:							Any		,     lowercase__						:							int		)						->   List[str]:
    '''simple docstring'''





    lowerCAmelCase_       :						List[Any]							= []
    lowerCAmelCase_       :						List[Any]							= fairseq_model.state_dict()

    lowerCAmelCase_       :						Dict							= hf_model.wavaveca.feature_extractor

    for name, value in fairseq_dict.items():
        lowerCAmelCase_       :						int							= False
        if "conv_layers" in name:
            load_conv_layer(
                UpperCamelCase_		,     UpperCamelCase_		,     UpperCamelCase_		,     UpperCamelCase_		,     hf_model.config.feat_extract_norm == """group"""		,     )
            lowerCAmelCase_       :						Optional[int]							= True
        else:
            lowerCAmelCase_       :						Optional[int]							= load_wavaveca_layer(UpperCamelCase_		,     UpperCamelCase_		,     UpperCamelCase_		)
        if not is_used:
            unused_weights.append(UpperCamelCase_		)

    logger.warning(f'Unused weights: {unused_weights}'		)
def       __UpperCamelCase					(      lowercase__						:							str		,     lowercase__						:							str		,     lowercase__						:							List[str]		,     lowercase__						:							Any		,     lowercase__						:							Union[str, Any]		)						->   Tuple:
    '''simple docstring'''





    lowerCAmelCase_       :						List[Any]							= full_name.split("""conv_layers."""		)[-1]
    lowerCAmelCase_       :						int							= name.split("""."""		)
    lowerCAmelCase_       :						List[str]							= int(items[0]		)
    lowerCAmelCase_       :						Tuple							= int(items[1]		)

    if type_id == 0:
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                raise ValueError(
                    f'{full_name} has size {value.shape}, but'
                    f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'		)
            lowerCAmelCase_       :						str							= value
            logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'		)
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                raise ValueError(
                    f'{full_name} has size {value.shape}, but'
                    f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'		)
            lowerCAmelCase_       :						Union[str, Any]							= value
            logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'		)
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                raise ValueError(
                    f'{full_name} has size {value.shape}, but'
                    f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'		)
            lowerCAmelCase_       :						Optional[int]							= value
            logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                raise ValueError(
                    f'{full_name} has size {value.shape}, but'
                    f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'		)
            lowerCAmelCase_       :						List[str]							= value
            logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
    else:
        unused_weights.append(UpperCamelCase_		)
@torch.no_grad()
def       __UpperCamelCase					(      lowercase__						:							List[Any]		,     lowercase__						:							Tuple		,     lowercase__						:							List[str]=None		,     lowercase__						:							Optional[int]=None		,     lowercase__						:							Tuple=True		,     lowercase__						:							Dict=False		)						->   int:
    '''simple docstring'''





    if config_path is not None:
        lowerCAmelCase_       :						List[Any]							= WavaVecaConfig.from_pretrained(UpperCamelCase_		)
    else:
        lowerCAmelCase_       :						Tuple							= WavaVecaConfig()

    if is_seq_class:
        lowerCAmelCase_       :						Any							= read_txt_into_dict(UpperCamelCase_		)
        lowerCAmelCase_       :						Dict							= idalabel
        lowerCAmelCase_       :						List[str]							= WavaVecaForSequenceClassification(UpperCamelCase_		)
        lowerCAmelCase_       :						Dict							= WavaVecaFeatureExtractor(
            feature_size=1		,     sampling_rate=16000		,     padding_value=0		,     do_normalize=UpperCamelCase_		,     return_attention_mask=UpperCamelCase_		,     )
        feature_extractor.save_pretrained(UpperCamelCase_		)

    elif is_finetuned:
        if dict_path:
            lowerCAmelCase_       :						Optional[int]							= Dictionary.load(UpperCamelCase_		)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            lowerCAmelCase_       :						Tuple							= target_dict.pad_index
            lowerCAmelCase_       :						int							= target_dict.bos_index
            lowerCAmelCase_       :						Tuple							= target_dict.eos_index
            lowerCAmelCase_       :						Dict							= len(target_dict.symbols		)
            lowerCAmelCase_       :						List[str]							= os.path.join(UpperCamelCase_		,     """vocab.json"""		)
            if not os.path.isdir(UpperCamelCase_		):
                logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(UpperCamelCase_		)		)
                return
            os.makedirs(UpperCamelCase_		,     exist_ok=UpperCamelCase_		)
            lowerCAmelCase_       :						Dict							= target_dict.indices

            # fairseq has the <pad> and <s> switched
            lowerCAmelCase_       :						Tuple							= 0
            lowerCAmelCase_       :						Optional[Any]							= 1
            with open(UpperCamelCase_		,     """w"""		,     encoding="""utf-8"""		) as vocab_handle:
                json.dump(UpperCamelCase_		,     UpperCamelCase_		)
            lowerCAmelCase_       :						List[Any]							= WavaVecaCTCTokenizer(
                UpperCamelCase_		,     unk_token=target_dict.unk_word		,     pad_token=target_dict.pad_word		,     bos_token=target_dict.bos_word		,     eos_token=target_dict.eos_word		,     word_delimiter_token="""|"""		,     do_lower_case=UpperCamelCase_		,     )
            lowerCAmelCase_       :						int							= True if config.feat_extract_norm == """layer""" else False
            lowerCAmelCase_       :						Union[str, Any]							= WavaVecaFeatureExtractor(
                feature_size=1		,     sampling_rate=16000		,     padding_value=0		,     do_normalize=UpperCamelCase_		,     return_attention_mask=UpperCamelCase_		,     )
            lowerCAmelCase_       :						Any							= WavaVecaProcessor(feature_extractor=UpperCamelCase_		,     tokenizer=UpperCamelCase_		)
            processor.save_pretrained(UpperCamelCase_		)

        lowerCAmelCase_       :						Optional[Any]							= WavaVecaForCTC(UpperCamelCase_		)
    else:
        lowerCAmelCase_       :						List[Any]							= WavaVecaForPreTraining(UpperCamelCase_		)

    if is_finetuned or is_seq_class:
        lowerCAmelCase_		,		lowerCAmelCase_		,		lowerCAmelCase_       :						Optional[Any]							= fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]		,     arg_overrides={"""data""": """/""".join(dict_path.split("""/"""		)[:-1]		)}		)
    else:
        lowerCAmelCase_       :						Tuple							= argparse.Namespace(task="""audio_pretraining"""		)
        lowerCAmelCase_       :						Optional[int]							= fairseq.tasks.setup_task(UpperCamelCase_		)

        lowerCAmelCase_		,		lowerCAmelCase_		,		lowerCAmelCase_       :						List[Any]							= fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		,     task=UpperCamelCase_		)

    lowerCAmelCase_       :						Optional[int]							= model[0].eval()

    recursively_load_weights(UpperCamelCase_		,     UpperCamelCase_		,     not is_finetuned		)

    hf_wavavec.save_pretrained(UpperCamelCase_		)


if __name__ == "__main__":
  __UpperCAmelCase							=   argparse.ArgumentParser()
  parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
  parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
  parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
  parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
  parser.add_argument(
      '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
  )
  parser.add_argument(
      '--is_seq_class',
      action='store_true',
      help='Whether the model to convert is a fine-tuned sequence classification model or not',
  )
  __UpperCAmelCase							=   parser.parse_args()

  __UpperCAmelCase							=   not args.not_finetuned and not args.is_seq_class
  convert_wavaveca_checkpoint(
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.dict_path,
      is_finetuned,
      args.is_seq_class,
  )







'''simple docstring'''

import os

SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  {'''I''': 1, '''V''': 5, '''X''': 1_0, '''L''': 5_0, '''C''': 1_0_0, '''D''': 5_0_0, '''M''': 1_0_0_0}






def 				a  (     UpperCamelCase_    :    str			)       ->	int:
		snake_case__	     =0

		snake_case__	     =0
		while index < len(UpperCamelCase_			) - 1:
				snake_case__	     =SYMBOLS[numerals[index]]
				snake_case__	     =SYMBOLS[numerals[index + 1]]
				if current_value < next_value:
						total_value -= current_value
				else:
						total_value += current_value
				index += 1
		total_value += SYMBOLS[numerals[index]]

		return total_value






def 				a  (     UpperCamelCase_    :    int			)       ->	str:
		snake_case__	     =''

		snake_case__	     =num // 1000
		numerals += m_count * "M"
		num %= 1000

		snake_case__	     =num // 100
		if c_count == 9:
				numerals += "CM"
				c_count -= 9
		elif c_count == 4:
				numerals += "CD"
				c_count -= 4
		if c_count >= 5:
				numerals += "D"
				c_count -= 5
		numerals += c_count * "C"
		num %= 100

		snake_case__	     =num // 10
		if x_count == 9:
				numerals += "XC"
				x_count -= 9
		elif x_count == 4:
				numerals += "XL"
				x_count -= 4
		if x_count >= 5:
				numerals += "L"
				x_count -= 5
		numerals += x_count * "X"
		num %= 10

		if num == 9:
				numerals += "IX"
				num -= 9
		elif num == 4:
				numerals += "IV"
				num -= 4
		if num >= 5:
				numerals += "V"
				num -= 5
		numerals += num * "I"

		return numerals






def 				a  (     UpperCamelCase_    :    str = "/p089_roman.txt"			)       ->	int:
		snake_case__	     =0

		with open(os.path.dirname(UpperCamelCase_			) + roman_numerals_filename			) as filea:
				snake_case__	     =filea.readlines()

		for line in lines:
				snake_case__	     =line.strip()
				snake_case__	     =parse_roman_numerals(UpperCamelCase_			)
				snake_case__	     =generate_roman_numerals(UpperCamelCase_			)
				savings += len(UpperCamelCase_			) - len(UpperCamelCase_			)

		return savings


if __name__ == "__main__":
			print(f"""{solution() = }""")




